candy_file <- "candy-data.csv"
candy = read.csv(candy_file, row.names=1)
head(candy)
Q1. How many different candy types are in this dataset?
nrow(candy)
## [1] 85
There are 85 types of candy.
Q2. How many fruity candy types are in the dataset?
sum(candy[,2])
## [1] 38
There are 38 fruity candy types.
candy["Twix", ]$winpercent
## [1] 81.64291
Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Sour Patch Kids", ]$winpercent
## [1] 59.864
Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat", ]$winpercent
## [1] 76.7686
Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars", ]$winpercent
## [1] 49.6535
library("skimr")
## Warning: package 'skimr' was built under R version 4.3.3
skim(candy)
| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
All percentages are continuous between 0 and 1 except winpercent looks to be on a different scale. It seems to be in % but not in decimal. All types column are either 0 or 1.
Q7. What do you think a zero and one represent for the candy$chocolate column?
A zero means this candy type does not contain chocolate and a one means it contains chocolate.
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
Q9. Is the distribution of winpercent values symmetrical?
No, it is not symmetrical.
Q10. Is the center of the distribution above or below 50%?
It is below 50%
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
chocolate<-candy$winpercent[as.logical(candy$chocolate)]
fruity<-candy$winpercent[as.logical(candy$fruity)]
t.test(chocolate,fruity)
##
## Welch Two Sample t-test
##
## data: chocolate and fruity
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974
chocolate candy is ranked higher.
Q12. Is this difference statistically significant?
Yes. Because the t value is 2.87 e-08 which is very very small.
Q13. What are the five least liked candy types in this set?
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
head(candy[order(candy$winpercent),], n=5)
candy %>% arrange(winpercent) %>% head(5)
Q14. What are the top 5 all time favorite candy types out of this set?
candy %>% arrange(desc(winpercent)) %>% head(5)
Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
ggplot(candy) +
aes(winpercent, rownames(candy)) +
geom_col()
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col()
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols)
Now, for the first time, using this plot we can answer questions like: -
Q17. What is the worst ranked chocolate candy?
Sixlets
library(ggrepel)
# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)
## Warning: ggrepel: 53 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Q19. Which candy type is the highest ranked in terms of winpercent for
the least money - i.e. offers the most bang for your buck?
Reese’s Miniatures.
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )
Nik L Nip is lease popular among the 5.
# Make a lollipop chart of pricepercent
ggplot(candy) +
aes(pricepercent, reorder(rownames(candy), pricepercent)) +
geom_segment(aes(yend = reorder(rownames(candy), pricepercent),
xend = 0), col="gray40") +
geom_point()
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.3.3
## corrplot 0.92 loaded
cij <- cor(candy)
corrplot(cij)
Q22. Examining this plot what two variables are anti-correlated
(i.e. have minus values)? Fruity and chocolate,fruity and bar, pluribus
and bar.
Q23. Similarly, what two variables are most positively correlated?
chocolate and winpercent, chocolate and bar.
pca <- prcomp(candy, scale=TRUE)
summary(pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000
plot(pca$x[, 1:2])
plot(pca$x[,1:2], col=my_cols, pch=16)
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])
p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")
## Warning: ggrepel: 40 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly(p)
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the
positive direction? Do these make sense to you? HINT. pluribus means the
candy comes in a bag or box of multiple candies.
Fruity and pluribus. Yes since fruity candys usually comes in a bag or box of multiple candies.